Shashank Kumar

Check out Jo! The privacy-centric personal AI that runs on your Mac and a private cloud machine they host for you. It knows your life, improves itself autonomously, chats via Telegram and WhatsApp, and NEVER SHARES YOUR DATA. Less "chatbot" and more "a second brain that does things."

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Forget the Silicon Valley Playbook. The 'Blinkit Effect' is the real secret behind India’s next $50B+ operational explosion. While some look at Blinkit as just a delivery app, VCs look at it as an Operational SEAL Team. From the high-pressure dark stores of 2021 to the disruptive founders of 2025, if you want to see where India’s execution-heavy decacorns are coming from, you look at the Blinkit Effect Here are my 5 VC-level insights into why the "Gurgaon Mafia" is currently rewriting the Indian execution playbook: 👇🏻 1) Prithvi Singh - Gameskraft Most gaming founders focus on "fun." Prithvi used his Blinkit-honed high-concurrency roots to build a platform that handles millions of real-time transactions without a glitch. He leveraged the "Zero-Latency" mindset to solve backend engineering hurdles that slower competitors simply couldn't code their way out of ⚡ 2) Varun Khurana - Otipy Varun realised early on that Agritech isn't a "farming" business; it’s a density problem. By treating every tomato like a time-sensitive delivery packet, he solved the "perishability" trap in farm-to-fork. Otipy’s hyper-local supply chain is a direct product of the 10-minute delivery DNA native to the Blinkit war room 🥦 3) Zaheer Khan - Ayu Health Proving that you can bring "Quick Commerce" rigor to healthcare. Zaheer didn't just focus on "Doctors"; he focused on a systemic pain point: fragmented hospital operations. By bringing "Blinkit-level" logistics to hospital networks, he is systematising an industry known for chaos, demonstrating the "Systemic-First" ambition of the modern Blinkit graduate 🏥 4) Manas Gupta - GobbleCube Mastering "Data-Driven Velocity." Manas took the intimidating world of e-commerce brand analytics and made it as fast as a flash sale. By focusing on real-time SKU-level precision and scalable data frameworks, he successfully navigated the "Insight Gap" for D2C brands that were drowning in slow data 📊 5) Abhishek Upadhyay - CubeAPM The invisible backbone of the 2026 enterprise. By launching an observability platform for complex digital stacks, Abhishek is ensuring that "Zero Downtime" is a reality for the global market. He proved that the Blinkit network doesn't just build apps; they build the infrastructure that keeps the world’s most complex systems running 🧊 The ➡️ Insight: The "Blinkit Effect" isn't about the bike or the bag; it’s about Urgency as a Culture. When your training ground is a 10-minute delivery window, your baseline for "fast" shifts. That psychological edge is what creates a high-velocity, $50B+ ecosystem. ➡️ The 2026 Blueprint: Don't just look for a good idea; look for an Execution Moat. The next unicorn won't just have a great product; it will have a founder who can survive the high-pressure "Blinkit" crucible and scale at 10x speed 📈

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Excited to share that Thread Factory has raised ₹1.5 Cr in pre-seed funding from ajvc to solve a massive problem in India’s fashion supply chain. We are building for a part of India’s $100B+ fashion market that is massive, but still works in a very old-school way. A lot of sourcing still happens through calls, WhatsApp messages, and scattered coordination between retailers and manufacturers. Which also means many buying decisions are still made without a clear sense of what is actually selling. And that gap becomes even sharper in the unorganised side of the market. While brands and online marketplaces have brought structure to one part of fashion retail, the remaining 65% still operates with very little technology. That is the part of the market we are building for. At Threadfactory, we are building a tech-first ecosystem for this supply chain – helping retailers source quality, trend-right designs more easily, while helping manufacturers plan production with better demand visibility. Our B2B platform allows retailers to discover products and place orders directly, while also capturing the commerce data needed to bring more intelligence into the system. Over the past year, we have seen early signs that this is solving something real: ~₹4 Cr ARR 60% average monthly repeat retailers 40+ manufacturers For me and Vivek, this round was a very meaningful milestone. We received a term sheet from Aviral within 10 days of our application, and that felt incredibly validating at this stage of the journey. This was when we did not even have a product, and were at zero revenue. What stood out even more was the clarity he brought to the conversation. He quickly understood the space and pushed us to think harder about what we are building and where it can go. The journey till here has not been easy. There has been a lot of learning, a lot of back-and-forth, and many moments of rethinking. But that process is also what made our conviction stronger. This round has helped us build a stronger team, improve the platform, and continue building for an industry that really needs better systems. Still early, but very excited for what lies ahead. 🚀 #threadfactory #ajvc #funding #b2b

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Artificial Intelligence, H1B Visa & The Test Of Accountability  I have been thinking about the brouhaha over the H-1B visa and believe a deeper look at Indian IT is in order. The sector’s under fire on two fronts:  →Due to the hike in H1B visa fees to $100,000. →From the disruption by artificial intelligence. Indians make up 70% of H1B visa recipients, with China trailing behind at 12%. The new fees surpass 60% of Indian workers’ annual salaries in the US, as they draw lower median wages, hitting us more than global peers. Indian IT companies could also see a drop in profit margin.  Besides that, AI is threatening to eliminate entry-level jobs. OpenAI CEO Sam Altman said that he's confident customer service jobs will be among the first to go, along with those who do routine coding tasks. It’s disheartening that highly skilled professionals — from developers to creators — who help build the system are under threat. They are stuck in bloated teams and face delays as they await endless approvals. While they bring value, creativity, and problem-solving skills — essential to enable differentiation in products and services — they are struggling to prove their worth. The service-model mindset is partly to blame. The role of the 'knowledge worker' has been diluted, from someone who applied expertise in solving complex problems and innovating to merely executing tasks. By including people in the decision-making chain who add no value but facilitate communication, we end up hindering agility. Corporate organisations are filled with pointless mid-management roles that reduce accountability. It’s possibly the result of an experiment gone wrong or an inability to fix the basics. Anthropologist David Graeber used the term "bullshit jobs" to describe such redundant positions in his 2018 book. In the era of AI, when information can be aggregated and distributed without intervention, it’s a wonder why we retain such roles. The lack of accountability that drives the current top-down system has to be fixed. So far, the skilled went abroad to become system builders, while India was left with mid-level staff. Now, we need to reframe India as a launchpad. Educational institutions, startups, and R&D labs must turn co-creators and lead skilled talent to use AI as a catalyst for creation. We need to set clear expectations for tech workers, provide resources and autonomy, give feedback, and establish a culture of ownership. The NEP 2020, which aims to integrate application-based learning within mainstream education, could prove to be a game-changer for early years, but companies also need to create pathways and opportunities for innovation at home. It’s only through such interventions that we can change the mindset that the only path to success lies abroad. So, yes, we do need to rethink some roles. But it’s not that of a highly skilled tech professional. #ArtificialIntelligence #H1BVisa #Software #IT #Technology

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Startup investments in Netherland hit glass ceiling. With some exceptions due to anomaly unicorn rounds in the Netherlands, we don't seem to go beyond the level that we experience since the covid period. Not just funds for scaling up are challenging to acquire, it is funding the whole journey from early stage to very late stage that proves challenging. Specifically, the amount of deals has been in a declining trend for many quarters in a row, hitting the lowest point this quarter again in 5 years. Government support has proven a backbone more and more in recent periods, raising the quest for more private capital. Imagine pension funds and savings accounts totalling close to EUR 2 trillion in the Netherlands, one would think there should be some opportunity here, but the system seems still clogged, or slow to say the least. Most worrying is that the general focus of governments throughout Europe is on scaling up and deep tech. If we don't pay a lot more attention to early stage and startups in general, dealflow will dry up at some point. From recent, soon to be published research, we know that a surprising large part of funded startups have one foot out the door. We do see movement from institutes and government to strengthen funds for the whole journey, but it needs to be bold, and it needs to continue to accelerate after upcoming elections. One positive element this quarter, we welcome our new unicorn Framer! There are more than enough candidates in the Netherlands for the next unicorn, decacorn or dare I say, trillion euro company! So, let's unlock. --- Full release in the comments. Report is a collaboration of the main Dutch national and regional ecosystem organisations. Thanks to: Dutch Startup Association Thomas Mensink Golden Egg Check Felix Zwart Nederlandse Vereniging van Participatiemaatschappijen (NVP) Orla Browne Nina Chaloum Peter van Sabben Dealroom.co Mustafa Torun Rinke Zonneveld Invest-NL Freek Welling Marc Jansen ROM-Nederland Dr. Job Andreoli Nyenrode Business University Myrthe Hooijman Constantijn Techleap Izzy Sayers Daniël van der Kolk KPMG Emerging Giants (NL)

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Excited to share this News, 888vc, Our Early-stage venture capital firm based in Bangalore, India has launched platfrom Gro8 backed by ₹175 crore (US$ 20M) fund to back startups focused on AI, deep tech, Enterprise SAAS and sustainability over the next three years. 💡 Key Focus Areas: 🔹 Average ticket size of ₹2–4 crores 🔹 Sector Agnostic Angel fund 🔹 Focus on globally scalable solutions 🔹 Launch of GRO8 angel platform for cross-border funding, mentorship & market access 📌 If you’re building in AI, deep-tech, or sustainability with ambitions to grow globally, let’s talk. Read more here: https://lnkd.in/gtUcYkrx #888vc #gro8 #venturecapital #angelinvestments #startups

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Moneyview didn’t hesitate to use Nano Banana for illustrations in its IPO prospectus. Here are some interesting tech stats from FY25 ⤵️ • Revenue: ₹2339 cr • Tech infra (A): ₹65 cr • Tech salaries (B): ₹117 cr • Tech salaries / Tech infra: 1.8x • Tech spend (A+B) as % of revenue: 8% PS: Numbers are rounded for better recall. #moneyview #ipo #tech #analysis

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So how did I go from googling every third word in meetings… to confidently owning product conversations in tech at Aurva ? I didn't learn hop on lots of technical jargons at once. I didn’t dive into system design docs on Day 1. Here’s what I did instead: 1️⃣ I focused on tech as a language, not a barrier. Just like finance has its own vocabulary, tech has its own too. I didn’t try to master everything. I just made it a rule: if a term came up twice, I had to understand it well enough to explain it to someone else. 2️⃣ I looked for how things connect. I wasn’t obsessed with “how this is built in code,” but with how the pieces fit together. I drew (very bad) diagrams until the product made sense in my head. 3️⃣ I asked dumb questions. (Often.) After initial understanding, I found the right engineers with whom I discussed ideas in details. And I’d trade context from my side such as how a customer behaves, how a deal might depend on this feature that made them see the product in a new way too. 4️⃣ I learned tech through product. Instead of learning “tech” for the sake of it, I used the product problem as my entry point. What does this feature need? What’s possible, what’s not? That’s how I naturally picked up the technical depth (right when I needed it). You don’t need to be the most technical person in the room. You need to be the one who connects the dots between customer, business, and tech. That’s what product is.

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I have attended numerous startup executive meetings. In tech brands, doing $500K ARR to $25M ARR. And I have seen one consistent play. There will always be one functional lead who will start with escalations from the outset, e.g. "We were never informed of this change....", "There is a serious lack of concern...", "We really don't have visibility in ...", and there will always be one other functional leader who would always step in and say "Sure. Let me look into this", "Can you share more detail about what you are saying" etc. On the surface, the first type of leader (say, Leader A) appears impressive. Bringing the problems to the surface, making sure everyone knows the problems etc. etc. The textbook leader. Does. Not. Work. For. Startups. Why? Because in startups, there is very little information asymmetry, and the CEO + leadership is aware of most things happening in the organization. Infact they are even aware of most employees in the org. This makes more sense in a corporation where things are often swept under the carpet, and it's necessary to bring them out into the open. But in a startup, this destroys whatever little camaraderie the team has managed to build, makes everyone feel wary of one another, and the focus is more on CYA (cover your ass) rather than pushing at the boundaries. So if you are a startup and want to know which leaders need to be given more responsibility and push, they are not the ones who bring problems to the table. Rather, folks who look at issues and try to solve them without making noise about them.

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After working with and within tech startups for almost 8 years now, I wouldn't start a tech product today. Instead, I'd start with building a service or audience that I'd then scale or productize with technology. The typical flow builders follow is: → Find a suboptimal experience humans face → Consider how technology could solve for it → Build an MVP, raise money → Run at a loss until the business scales to profitability What's less "sexy" but much more likely to drive immediate and long term success is starting as a service provider. Say you notice that brands and celebrities want to work together, but they need a way to meet. Most people might consider creating a marketplace on a digital platform where users log in as either celebrities or brands and connect with each other directly in the platform. The company monetizes through a % commission from each deal closed or perhaps a monthly fee to be listed. The issue with this is that it requires a ton of upfront capital to build, and then you have to make sure the marketplace is constantly full/active enough for people to get something out of it otherwise your churn will quickly run you dry. What would I do instead? Realistically if I had this idea in the first place I'd likely be really familiar with the industry. I'd likely (hopefully lol) have an existing network of celebrities and brands to immediately incentivize to the platform, or I'd have a partner who would have those connections. Rather than spend months to years building a tech platform that requires economies of scale to solve the issue, I would instead start as a consultant/service provider who does white glove matchmaking between celebrities and brands on a case by case basis. I would create a backend roster of my contacts in each category and collect as much metadata as possible on each team. I would meet with high intent celebrities (starting with the celebrities instead of the brands because it's more likely that the power dynamic is in the celebrity's favor) and collect information on the type of deal they're looking for. I would then meet with a handful of high intent brands I know are looking for something compatible. When a deal closes, I would take a commission (or charge an hourly rate for my time even if the deal doesn't close, depending on how likely I think it is for me to effectively secure deals). After building a few case studies that I've found product market fit, I would think about how to productize my workstream with ops and AI. Can I use AI to enrich my roster database or even analyze which brands could be best for the client I'm working with? Eventually, after over a year of doing this, would it be more efficient for me to put all my clients (who are already active and engaged) onto the marketplace that I build using the capital made by my existing agency? Something I've been thinking about for a few years! What do you think?

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Some interesting thoughts on running a YC-like Accelerator in India. https://lnkd.in/gqFZFuD2 A large part of Y Combinator's success is 2 things which are touched on in the article but not emphasised enough. 1. Its momentum today is because of its initial activation energy that let it take off like a rocket. And that activation energy was Paul Graham. He already had a captive audience with the right kind of people who could be successful in an accelerator, through his thoughtful (and at the time heretical) essays on Lisp and Python and Hacker culture in general. Even the name, Y-Combinator is a nerdy reference in functional programming and self-selected for a certain type of person. 2. Like his essays, his founders were heretics. Not traditional investors. They were themselves founders who could cut through the noise and focus on the basics. The culture they created of keeping things simple allowed them to continuously succeed until they put together enough momentum to sustain themselves on inertia.

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There's a lot to learn for founders in this note and IDfy journey. But I think there's a learning here for folks in VC firms as well - please don't tell founders that they are wasting their life with the company they are building(even if you mean well).

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Two brothers from Bihar just built an AI startup that went from $50M to $100M ARR in 30 days. Let that sink in. Meet Mukund Jha and Madhav Jha, identical twins who are now building one of the fastest-growing AI startups globally Emergent Labs. But this isn’t a typical success story. Before this breakout, Mukund had already failed in two startups. No big exits, no headlines — just hard lessons that most founders never recover from. What changed everything was their insight. While the world was busy experimenting with LLMs and ChatGPT, the Jha brothers believed something deeper — that AI agents, not just tools, will become the future of software. Systems that don’t just respond, but actually execute tasks. That insight led them to ask a simple but powerful question: what if anyone could build a full app just by describing it? In 2025, they launched Emergent through Y Combinator. A platform where you type your idea, and AI builds everything—frontend, backend, APIs, deployment. No coding required. They called it “vibe coding.” What followed wasn’t growth, it was an explosion. Developers, creators, and even non-technical users started building apps. The product itself became the distribution engine. Within 3 months, they hit $15M ARR. Then came the real breakout — scaling from $50M to $100M ARR in just 30 days. Today, Emergent has over 5 million users across 190 countries, with millions of apps built on the platform. This is not just a company growing fast; this is a category forming in real time. Two failed startups didn’t end Mukund’s journey; they sharpened it. Because in the end, breakout companies are rarely about luck. They are built at the intersection of timing, deep insight, and the ability to stay in the game long enough. #startup And maybe that’s the real lesson for Indian #founders. Don’t just build for India. Build from India, for the world.

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#BookRecommendation #ByteDance I am a staunch believer that the India technology ecosystem can benefit immensely from key learnings ("What to do?" and also, "what NOT to do?") from mature markets like China (one of the key reasons PROMAFT exists today!). 'Attention Factory', the book does a brilliant job of deeply researching the evolution of ByteDance - a company that has been rather obscure for it scale and impact. The book immaculately captures - 1. Thinking hard about the 'Consumer need' - Social network or Content Platform - using data to prove the same. 2. Evolution to 'Product-Market-Fit' - they were not the first to market! 3. Investing behind scalable technology - a recommendation system that 2x'ed' retention rate and usage in a matter of weeks 4. Rapidly scaling post 'Product-Market-Fit' - customer acquisition, M&A, offline activation - leaving gargantuan competitors like Tencent behind Super valuable for any entrepreneur looking to build & scale globally. Can't wait to re-read this again. Matthew Brennan

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Six months in. Met some of the best builders and backers in the business. Here's a snapshot from a few. On building from zero: Tim Chong at Yonder has 65% DAU on a rewards card. For context, WhatsApp is at ~80%. Early chaos, no funding, no team, regulations at every turn. Yet, each obstacle just made him, the team and the product sharper. Tim radiates the kind of founder energy that makes you want to go build immediately. Michael Reitblat at Forter has quietly built a fraud prevention behemoth, managing $1.5T+ in transactions. 620M shoppers protected. Started from nothing, now Nordstrom, Asos, McDonald's all run on it. The incumbents had every advantage. The grit to build a real challenger product and win..unreal. Yaron Inger at Lightricks($250M+ ARR): started as a photo editing app (Facetune) in 2013. Twelve years. Multiple full reinventions, shipping at speed, treating every prior version as a launchpad not a legacy. Today, it is a top-ranked open source video generation model on HuggingFace. Their product demo was one of thosewhere you walk out and quietly recalibrate. On the AI DeepTech frontier, up close: I've been watching Waymo's London tests across Baker street. The real story might be underneath: Kyle Esecson at Wayve(raised $1.5B+) reframed something for me. While Waymo gets the headlines, Wayve is quietly licensing an end-to-end AI foundation model directly to OEMs, no geo-fencing, no HD maps, drop it anywhere and it drives. The bet isn't owning the fleet. It's owning the software inside every fleet. Synthesia ($100M+ ARR) started with David Beckham speaking 9 languages in a Heineken ad. Today they power enterprise video at Nike, Walmart, Accenture. PLG did the work: individual users became internal champions, champions became company-wide deals. Next: two-way multilingual avatars. AppsFlyer sits between every major app and every major ad platform in the world. 950M+ app installs. 180B ad views. 15,000 brands. AppsFlyer's unbiased cross-channel measurement is quietly one of the most valuable products in marketing infrastructure. On PE: Sitting across from fund managers at Volpi Capital StepStone Group Cinven AnaCap hearing how they're actually deploying capital right now, in this rate environment, with DPI pressure, with GPs scrambling to exit, was truly invaluable. StepStone's LP data flywheel (200K+ investment data points across thousands of GPs) means you've essentially seen every deal, every vintage, every mistake, before deploying capital AnaCap gave a session I won't forget. A live case study on a real investment, built on 20 years of European financial services pattern recognition - the kind of thing you can't manufacture in a classroom. Truly grateful to everyone who gave us their time! #LBS #MBA #AI

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